The effect of using a large language model to respond to patient messages

作者：Shan Chen, Marco Guevara-Vega, Shalini Moningi, Frank Hoebers, Hesham Elhalawani, Benjamin H. Kann, Fallon Chipidza, Jonathan Leeman, Hugo J.W.L. Aerts, Timothy Miller, Guergana K Savova, Jack Gallifant, Leo A Celi, Raymond H Mak, Maryam B. Lustberg, Majid Afshar, Danielle S. Bitterman · 发表于：The Lancet Digital Health · 年份：2024 · DOI：10.1016/s2589-7500(24)00060-8 · 被引用次数：135 · 研究领域：Artificial Intelligence in Healthcare and Education、Electronic Health Records Systems、Patient-Provider Communication in Healthcare

The relentless increase in administrative responsibilities, amplified by electronic health record (EHR) systems, has diverted clinician attention from direct patient care, fuelling burnout.1 In response, large language models (LLMs) are being adopted to streamline clinical and administrative tasks. Notably, Epic is currently leveraging OpenAI's ChatGPT models, including GPT-4, for electronic messaging via online portals.2 The volume of patient portal messaging has escalated in the past 5–10 years,3 and general-purpose LLMs are being deployed to manage this burden.